GCM Hindcasts of SST Forced Climate Variability over Agriculturally Intensive Regions |
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| Authors: | Leonard M Druyan Kathryn P Shah Mark A Chandler David Rind |
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| Institution: | (1) NASA/Goddard Institute for Space Studies, 2880 Broadway, New York, 10025, U.S.A.;(2) Center for Climate Systems Research, Columbia University, New York, 10025, U.S.A. |
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| Abstract: | The NASA/Goddard Institute for Space Studies (GISS) climatemodel is forced with globally observed sea-surfacetemperatures (SST) in five simulations, 1969–1991,with individual runs beginning from altered initialatmospheric conditions. The interannual variability ofmodeled anomalies of the Southern Oscillation Index,mid-tropospheric temperatures, 850 mb zonal winds andOutgoing Longwave Radiation over the tropical PacificOcean, which has the largest SST anomaly forcing, arestrongly correlated with observed trends which reflectENSO cycles. The model's rainfall variability overthree agriculturally intensive regions, two tropicaland one mid-latitude, is investigated in order toevaluate the potential usefulness of GCM predictionsfor agricultural planning. The correct sign ofZimbabwe seasonal precipitation anomalies was hindcastwithin a useful range of consensus only for selectseasons corresponding to extreme ENSO events for whichanomalous circulation patterns were ratherrealistically simulated. The correlation betweenhindcasts of Nordeste monthly precipitation andobservations increases with time smoothing, reaching0.64 for 5-month running means. Consensus betweenindividual runs is directly proportional to theabsolute value of Niño3 SST so that during ElNiño and La Niña years most simulations agreeon the sign of predicted Nordeste rainfall anomalies.We show that during selected seasons the uppertropospheric divergent circulation and near surfacemeridional displacements of the ITCZ are realisticallyrepresented by the ensemble mean of the simulations.This realistic simulation of both the synopticmechanisms and the resulting precipitation changesincreases confidence in the GCM's potential forseasonal climate prediction. |
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